Quantifying the Impact of Hydrological Connectivity on Salt Marsh Vegetation in the Liao River Delta Wetland.

Salt marshes play a critical role in ecological functioning and have significant economic value. Hydrological elements are considered to be one of the major contributors to salt marsh degradation. However, how hydrological connectivity affects salt marshes remains poorly investigated at fine scales. This paper used spatial analysis and statistical methods to investigate the impact of hydrological connectivity on the spatial and temporal distribution characteristics of salt marsh vegetation in two natural succession areas of the Liao River Delta wetland in 2020 and 2021 by selecting vegetation area, NDVI, tidal creeks area, distance to tidal creeks, and the Index of Connectivity, using 1 m Gaofen-2 data and 0.2 m aerial topographic data. The study found that the area and growth status of vegetation and the overall connectivity in 2021 were better than that in 2020, while the west bank of the Liao River was better than that on the east bank. Phragmites australis showed a round island distribution pattern primarily at the end of tidal creeks. The differences between different hydrological connectivity and vegetation area were significant in 2021. The vegetation area was the largest under poor and moderate connectivity. We also found that within a distance range of 0-6 m from tidal creeks, the vegetation area increased with increasing distance, but beyond 6 m, the vegetation area decreased with increasing distance. Our results showed that poor and moderate connectivity conditions were more suitable for vegetation growth. The threshold value of 6 m can provide an important reference for wetland vegetation restoration in the Liao River Delta wetland. Supplementary Information: The online version contains supplementary material available at 10.1007/s13157-023-01693-4.

Depositional dynamics and vegetation succession in self-organizing processes of deltaic marshes.

Global deltaic marshes are currently facing a multitude of pressures, including insufficient sediment supply, rising sea levels, and habitat loss. Consequently, unraveling the internal regulatory mechanisms within deltaic marshes is of paramount importance. Here, we harness years of observational data and high-resolution numerical models to uncover depositional dynamics and vegetation succession in self-organizing processes of deltaic marshes. Our findings indicate that the colonization of salt marsh vegetation triggered a robust phase of growth in the initial stages of river deltas formation. However, as vertical accretion intensifies and inundation decreases, the delta is driven towards a state of critical slowing down due to insufficient sediment supply. We have captured a pivotal turning point in the evolution of deltaic marshes. In accordance with the critical submergence threshold we have established, when the inundation time of deltaic marshes exceeds 0.97 h/d, these salt marsh platforms sustain a higher annual growth rate. Conversely, when the inundation time of deltaic marshes falls below 0.97 h/d, the interannual accretion rate continues to decrease. Our research reveals that, in the absence of human disturbances, the deposition rate in deltaic marshes transitions from growth to decline. During this period, the delta undergoes an interesting succession of pioneer salt marshes (Suaeda salsa) and high-elevation salt marshes (Phragmites australis). Even without reductions in sediment input due to human activities, the vertical deposition rate within deltaic marshes will still shift from acceleration to deceleration under the influence of this internal negative feedback regulation. This adaptive capacity of marshes may foreshadow that when observing a slowdown in vertical accretion on deltaic marsh platforms, it cannot be solely attributed to reductions in sediment input caused by human activities.

Development and evaluation of a GPU-based coupled three-dimensional hydrodynamic and water quality model.

In this study, a graphics processing unit (GPU)-based three-dimensional coupled hydrodynamic and water quality numerical model (GPUOM-WQ) was developed for the first time, which introduces pollution sources of atmospheric deposition, aquaculture wastewater, and oil platform emission to describe marine pollution comprehensively. A test case with analytical solutions and a real case with measured data were used to validate the accuracy of GPUOM-WQ. Simulation results indicate that the maximum error between the numerical and analytical solutions is 0.9 %, and the average relative error between simulated and measured values of 5 water quality variables at 38 stations in spring, summer, fall and winter is 14.63 %. In the real case simulation, GPUOM-WQ accelerates the computation 62.48 times, which is 3.23 times faster than in 64-core central processing unit (CPU) parallel mode. This study makes it possible to accurately simulate the marine water quality variation and spatiotemporal distribution in a high-resolution and efficient way.

Parameter optimization method for the water quality dynamic model based on data-driven theory.

Parameter optimization is important for developing a water quality dynamic model. In this study, we applied data-driven method to select and optimize parameters for a complex three-dimensional water quality model. First, a data-driven model was developed to train the response relationship between phytoplankton and environmental factors based on the measured data. Second, an eight-variable water quality dynamic model was established and coupled to a physical model. Parameter sensitivity analysis was investigated by changing parameter values individually in an assigned range. The above results served as guidelines for the control parameter selection and the simulated result verification. Finally, using the data-driven model to approximate the computational water quality model, we employed the Particle Swarm Optimization (PSO) algorithm to optimize the control parameters. The optimization routines and results were analyzed and discussed based on the establishment of the water quality model in Xiangshan Bay (XSB).